Dataset Information

Polyadenylation ligation-mediated sequencing (PALM-Seq) characterizes cell-free coding and non-coding RNAs in human biofluids.

ABSTRACT:

Background

Cell-free messenger RNA (cf-mRNA) and long non-coding RNA (cf-lncRNA) are becoming increasingly important in liquid biopsy by providing biomarkers for disease prediction, diagnosis and prognosis, but the simultaneous characterization of coding and non-coding RNAs in human biofluids remains challenging.

Methods

Here, we developed polyadenylation ligation-mediated sequencing (PALM-Seq), an RNA sequencing strategy employing treatment of RNA with T4 polynucleotide kinase to generate cell-free RNA (cfRNA) fragments with 5' phosphate and 3' hydroxyl and RNase H to deplete abundant RNAs, achieving simultaneous quantification and characterization of cfRNAs.

Results

Using PALM-Seq, we successfully identified well-known differentially abundant mRNA, lncRNA and microRNA in the blood plasma of pregnant women. We further characterized cfRNAs in blood plasma, saliva, urine, seminal plasma and amniotic fluid and found that the detected numbers of different RNA biotypes varied with body fluids. The profiles of cf-mRNA reflected the function of originated tissues, and immune cells significantly contributed RNA to blood plasma and saliva. Short fragments (<50 nt) of mRNA and lncRNA were major in biofluids, whereas seminal plasma and amniotic fluid tended to retain long RNA. Body fluids showed distinct preferences of pyrimidine at the 3' end and adenine at the 5' end of cf-mRNA and cf-lncRNA, which were correlated with the proportions of short fragments.

Conclusion

Together, PALM-Seq enables a simultaneous characterization of cf-mRNA and cf-lncRNA, contributing to elucidating the biology and promoting the application of cfRNAs.

SUBMITTER: Liu Z

PROVIDER: S-EPMC9299576 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Recent advances in sequencing technology have opened a new era in RNA studies. Novel types of RNAs such as long non-coding RNAs (lncRNAs) have been discovered by transcriptomic sequencing and some lncRNAs have been found to play essential roles in biological processes. However, only limited information is available for lncRNAs in Drosophila melanogaster, an important model organism. Therefore, the characterization of lncRNAs and identification of new lncRNAs in D. melanogaster is an important area of research. Moreover, there is an increasing interest in the use of ChIP-seq data (H3K4me3, H3K36me3 and Pol II) to detect signatures of active transcription for reported lncRNAs.We have developed a computational approach to identify new lncRNAs from two tissue-specific RNA-seq datasets using the poly(A)-enriched and the ribo-zero method, respectively. In our results, we identified 462 novel lncRNA transcripts, which we combined with 4137 previously published lncRNA transcripts into a curated dataset. We then utilized 61 RNA-seq and 32 ChIP-seq datasets to improve the annotation of the curated lncRNAs with regards to transcriptional direction, exon regions, classification, expression in the brain, possession of a poly(A) tail, and presence of conventional chromatin signatures. Furthermore, we used 30 time-course RNA-seq datasets and 32 ChIP-seq datasets to investigate whether the lncRNAs reported by RNA-seq have active transcription signatures. The results showed that more than half of the reported lncRNAs did not have chromatin signatures related to active transcription. To clarify this issue, we conducted RT-qPCR experiments and found that ~95.24% of the selected lncRNAs were truly transcribed, regardless of whether they were associated with active chromatin signatures or not.In this study, we discovered a large number of novel lncRNAs, which suggests that many remain to be identified in D. melanogaster. For the lncRNAs that are known, we improved their characterization by integrating a large number of sequencing datasets (93 sets in total) from multiple sources (lncRNAs, RNA-seq and ChIP-seq). The RT-qPCR experiments demonstrated that RNA-seq is a reliable platform to discover lncRNAs. This set of curated lncRNAs with improved annotations can serve as an important resource for investigating the function of lncRNAs in D. melanogaster.

Project description:BACKGROUND:Long non-coding RNAs (lncRNAs) are increasingly recognized as regulators of tissue-specific cellular functions and have been shown to regulate transcriptional and translational processes, acting as signals, decoys, guides, and scaffolds. It has been suggested that some lncRNAs act in cis to regulate the expression of neighboring protein-coding genes (PCGs) in a mechanism that fine-tunes gene expression. Gut microbiome is increasingly recognized as a regulator of development, inflammation, host metabolic processes, and xenobiotic metabolism. However, there is little known regarding whether the gut microbiome modulates lncRNA gene expression in various host metabolic organs. The goals of this study were to 1) characterize the tissue-specific expression of lncRNAs and 2) identify and annotate lncRNAs differentially regulated in the absence of gut microbiome. RESULTS:Total RNA was isolated from various tissues (liver, duodenum, jejunum, ileum, colon, brown adipose tissue, white adipose tissue, and skeletal muscle) from adult male conventional and germ-free mice (n =?3 per group). RNA-Seq was conducted and reads were mapped to the mouse reference genome (mm10) using HISAT. Transcript abundance and differential expression was determined with Cufflinks using the reference databases NONCODE 2016 for lncRNAs and UCSC mm10 for PCGs. Although the constitutive expression of lncRNAs was ubiquitous within the enterohepatic (liver and intestine) and the peripheral metabolic tissues (fat and muscle) in conventional mice, differential expression of lncRNAs by lack of gut microbiota was highly tissue specific. Interestingly, the majority of gut microbiota-regulated lncRNAs were in jejunum. Most lncRNAs were co-regulated with neighboring PCGs. STRING analysis showed that differentially expressed PCGs in proximity to lncRNAs form tissue-specific networks, suggesting that lncRNAs may interact with gut microbiota/microbial metabolites to regulate tissue-specific functions. CONCLUSIONS:This study is among the first to demonstrate that gut microbiota critically regulates the expression of lncRNAs not only locally in intestine but also remotely in other metabolic organs, suggesting that common transcriptional machinery may be shared to transcribe lncRNA-PCG pairs, and lncRNAs may interact with PCGs to regulate tissue-specific pathways.